A preliminary phylogenetic study of late Palaeozoic spiriferoid brachiopods using cladistic and Bayesian approaches

Abstract The brachiopod Superfamily Spiriferoidea diversified greatly and was widely distributed in the late Palaeozoic (Carboniferous–Permian), and yet its phylogeny has been seldom investigated with analytical methods. This is reflected in the current flux of very different classification schemes for this superfamily. This paper provides the first attempt to investigate the phylogenetic relationships of spiriferoid brachiopods through both cladistic and Bayesian analyses involving 24 discrete and continuous characters. The continuous characters, from morphometric data, have been separately discretized using the gap weighting method, and the ‘as such’ option in TNT. Our results highlight the potential significance of continuous characters in reconstructing and elucidating phylogenies, as much as qualitative characters. Building on the outcomes of the analyses, we also briefly evaluate existing classification schemes of Spiriferoidea. We found that none of the existing classifications fully reflect the phylogeny properly; major families within the superfamily, such as Spiriferidae, Choristitidae, and Trigonotretidae, turned out to be polyphyletic. Although this study is considered preliminary, due to the selection of and restriction to certain taxa, combined with the use of a relatively small number of characters, it nevertheless demonstrates that potentially the true phylogenetic relationships of spiriferoid taxa sharply contrast with any of the existing classification schemes. This highlights the need to develop an alternative scheme that takes into account a more comprehensive range of phylogenetic variables.

Can phylogeny predict chemical diversity and potential medicinal activity of plants? A case study of amaryllidaceae

Background During evolution, plants and other organisms have developed a diversity of chemical defences, leading to the evolution of various groups of specialized metabolites selected for their endogenous biological function. A correlation between phylogeny and biosynthetic pathways could offer a predictive approach enabling more efficient selection of plants for the development of traditional medicine and lead discovery. However, this relationship has rarely been rigorously tested and the potential predictive power is consequently unknown.
Results We produced a phylogenetic hypothesis for the medicinally important plant subfamily Amaryllidoideae (Amaryllidaceae) based on parsimony and Bayesian analysis of nuclear, plastid, and mitochondrial DNA sequences of over 100 species. We tested if alkaloid diversity and activity in bioassays related to the central nervous system are significantly correlated with phylogeny and found evidence for a significant phylogenetic signal in these traits, although the effect is not strong.
Conclusions Several genera are non-monophyletic emphasizing the importance of using phylogeny for interpretation of character distribution. Alkaloid diversity and in vitro inhibition of acetylcholinesterase (AChE) and binding to the serotonin reuptake transporter (SERT) are significantly correlated with phylogeny. This has implications for the use of phylogenies to interpret chemical evolution and biosynthetic pathways, to select candidate taxa for lead discovery, and to make recommendations for policies regarding traditional use and conservation priorities.

Complex responses of birds to landscape-level fire extent, fire severity and environmental drivers

Aim: To quantify bird responses to a large unplanned fire, taking into consideration landscape-level fire severity and extent, pre-fire site detection frequency and environmental gradients. Location: South-eastern Australia. Methods: A major wildfire in 2009 coincided with a long-term study of birds and provided a rare opportunity to quantify bird responses to wildfire. Using hierarchical Bayesian analysis, we modelled bird species richness and the detection frequency of individual species in response to a suite of explanatory variables, including (1) landscape-level fire severity and extent (2) pre-fire detection frequency, (3) site-level vegetation density and (4) environmental variables (e.g. elevation and topography). Results: Landscape-level fire severity had strong effects on bird species richness and the detection frequency of the majority of bird species. These effects varied markedly between species; most responded negatively to amount of severely burned forest in the landscape, one negatively to the amount of moderately burned forest and one responded negatively to the total area of burned forest. Only one species - the Flame Robin - responded positively to the amount of burned forest. Relationships with landscape-scale fire extent changed over time for one species - the Brown Thornbill - with initially depressed rates of detection recovering after just 2 years. The majority of species were significantly more likely to be detected in burned areas if they have been recorded there prior to the fire. Main conclusions: Birds responded strongly to the severity and spatial extent of fire. They also exhibited strong site fidelity even after severe wildfire which causes profound changes in vegetation cover - a response likely influenced by environmental features such as elevation and topography.

Phylogeography of the antilopine wallaroo (Macropus antilopinus) across tropical northern Australia

The distribution of antilopine wallaroo, Macropus antilopinus, is marked by a break in the species’ range between Queensland and the Northern Territory, coinciding with the Carpentarian barrier. Previous work on M. antilopinus revealed limited genetic differentiation between the Northern Territory and Queensland M. antilopinus populations across this barrier. The study also identified a number of divergent lineages in the Northern Territory, but was unable to elucidate any geographic structure. Here, we re-examine these results to (1) determine phylogeographic patterns across the range of M. antilopinus and (2) infer the biogeographic barriers associated with these patterns. The tropical savannahs of northern Australia: from the Cape York Peninsula in the east, to the Kimberley in the west. We examined phylogeographic patterns in M. antilopinus using a larger number of samples and three mtDNA genes: NADH dehydrogenase subunit 2, cytochrome b, and the control region. Two datasets were generated and analyzed: (1) a subset of samples with all three mtDNA regions concatenated together and (2) all samples for just control region sequences that included samples from the previous study. Analysis included generating phylogenetic trees based on Bayesian analysis and intraspecific median-joining networks. The contemporary spatial structure of M. antilopinus mtDNA lineages revealed five shallow clades and a sixth, divergent lineage. The genetic differences that we found between Queensland and Northern Territory M. antilopinus samples confirmed the split in the geographic distribution of the species. We also found weak genetic differentiation between Northern Territory samples and those from the Kimberley region of Western Australia, possibly due to the Kimberley Plateau–Arnhem Land barrier. Within the Northern Territory, two clades appear to be parapatric in the west, while another two clades are broadly sympatric across the Northern Territory. MtDNA diversity of M. antilopinus revealed an unexpectedly complex evolutionary history involving multiple sympatric and parapatric mtDNA clades across northern Australia. These phylogeographic patterns highlight the importance of investigating genetic variation across distributions of species and integrating this information into biodiversity conservation.

Connectivity, online social capital, and mood : a Bayesian nonparametric analysis

Social capital indicative of community interaction and support is intrinsically linked to mental health. Increasing online presence is now the norm. Whilst social capital and its impact on social networks has been examined, its underlying connection to emotional response such as mood, has not been investigated. This paper studies this phenomena, revisiting the concept of “online social capital†in social media communities using measurable aspects of social participation and social support. We establish the link between online capital derived from social media and mood, demonstrating results for different cohorts of social capital and social connectivity. We use novel Bayesian nonparametric factor analysis to extract the shared and individual factors in mood transition across groups of users of different levels of connectivity, quantifying patterns and degree of mood transitions. Using more than 1.6 million users from Live Journal, we show quantitatively that groups with lower social capital have fewer positive moods and more negative moods, than groups with higher social capital. We show similar effects in mood transitions. We establish a framework of how social media can be used as a barometer for mood. The significance lies in the importance of online social capital to mental well-being in overall. In establishing the link between mood and social capital in online communities, this work may suggest the foundation of new systems to monitor online mental well-being.

Bayesian hierarchical modeling of the temporal dynamics of subjective well-being: A 10 year longitudinal analysis

This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (. n=. 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

Input usage, output mix and industry deregulation: an analysis of the Australian dairy manufacturing industry

In this paper we estimate a Translog output distance function for a balanced panel of state level data for the Australian dairy processing sector. We estimate a fixed effects specification employing Bayesian methods, with and without the imposition of monotonicity and curvature restrictions. Our results indicate that Tasmania and Victoria are the most technically efficient states with New South Wales being the least efficient. The imposition of theoretical restrictions marginally affects the results especially with respect to estimates of technical change and industry deregulation. Importantly, our bias estimates show changes in both input use and output mix that result from deregulation. Specifically, we find that deregulation has positively biased the production of butter, cheese and powders.

Identifying dependency between secure messages for protocol analysis

Collusion attack has been recognized as a key issue in e-commerce systems and increasingly attracted people’s attention for quite some time in the literatures of information security. Regardless of the wide application of security protocol, this attack has been largely ignored in the protocol analysis. There is a lack of efficient and intuitive approaches to identify this attack since it is usually hidden and uneasy to find. Thus, this article addresses this critical issue using a compact and intuitive Bayesian network (BN)-based scheme. It assists in not only discovering the secure messages that may lead to the attack but also providing the degree of dependency to measure the occurrence of collusion attack. The experimental results demonstrate that our approaches are useful to detect the collusion attack in secure messages and enhance the protocol analysis.

A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.

A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

Joint analysis of multiple data sources is becoming increasingly popular in transfer learning, multi-task learning and cross-domain data mining. One promising approach to model the data jointly is through learning the shared and individual factor subspaces. However, performance of this approach depends on the subspace dimensionalities and the level of sharing needs to be specified a priori. To this end, we propose a nonparametric joint factor analysis framework for modeling multiple related data sources. Our model utilizes the hierarchical beta process as a nonparametric prior to automatically infer the number of shared and individual factors. For posterior inference, we provide a Gibbs sampling scheme using auxiliary variables. The effectiveness of the proposed framework is validated through its application on two real world problems - transfer learning in text and image retrieval.

Model based analysis of fMRI/EEG data

This thesis addresses two major topics in neuroscience literature and drawbacks from existing literature are addressed by utilising state space models and Bayesian estimation techniques. Particle filter-based joint estimation of the physiological model for time-series analysis of fMRI data is demonstrated first in the thesis and secondly the Granger causality-based effective connectivity analysis of EEG data is investigated.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.